The A-C (Drug-Disease) interactions inferred using B (gene) terms are evaluated by examining how many inferred interactions are matched in well-known interaction databases such as PharmGKB and CTD.
This work considers how the inferred mixing state of diffusive and advective diffusive systems will vary over time when the solute masses are not constant over time.
We describe how the inferred substitution models can be used to generate scoring matrices suitable for alignment and similarity searches.
The future experiments will consider cell mobility assay to test how these inferred key drivers impact cancer cell mobility that drives metastasis.
We further demonstrate how the inferred progression dynamics can be used to improve genetics-based survival predictions which could support diagnostics and prognosis.
For several miRNAs (miR-21, miR-155, miR-424, and miR-17-92 miR-17-92 miR-17-92 inferred transcriptional regulation impacts monocytic differentiation.
Tree visualization in systematic and evolutionary biology is often done with tree comparison in mind, e.g., how does the inferred tree change when tuning this or that parameter in the inference?
This might be due to the fact that MST and eMST identify physical interactions on the basis of maximum spanning trees which take into account how well the inferred direct interactions connect the entire network.
Given an estimated ancestor gene order G M, we can use the breakpoint distance between G M and G0 as a measurement of how close the inferred ancestor is to the true ancestor.
By using this regression model, each sequence-predicted miRNA-mRNA interaction is assigned one coefficient; this coefficient represents how much the inferred activity profile of that miRNA contributes to predicting that mRNA's "down-regulation" profile (see Methods) when considering the activity profiles of all other miRNAs predicted to target the mRNA.
